The deeper the convolutional network is, the more important is the vanishing gradient problem. To prevent this problem, He et al. [1]} introduced the ResNet architecture which introduces a residual module as shown in Figure REF . Instead of using four branches, as does the Inception module, only uses two branches: left and right branches. The left branch consists on a series of convolutions (1x1, 3x3), activations and batch normalizations. On the other hand, the right branch is a “linear shortcut”, which connects the module input (or output from previous layer) with the bottom of the module where it is added to the output of the left branch. This addition, known as an identity mapping, turns out the network to learn faster and with larger learning rates, allowing to train networks that are substantially deeper than previous architectures, reducing the problem of the vanishing gradient problem.
